[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71AT75VHACADF1M506P7N0F":3,"skill-same:01M71AT75VHACADF1M506P7N0F":69},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":49,"commitSha":50,"skillDirectory":51,"forksCount":14,"repositoryHtmlUrl":52,"repositoryHomepageUrl":6,"ownerHtmlUrl":53,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":54,"install":55,"resources":68},"01M71AT75VHACADF1M506P7N0F","self-improving-agent",null,"Autonomous agent that continuously improves its own capabilities through reflection, experimentation, and learning. Builds upon the Evolver meta-skill with additional focus on goal-directed self-modification, capability expansion, and performance optimization.","sapsapshen\u002Fai-memory-router","sapsapshen","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F20347959?v=4","01M6XDQEF1DP50DKF37H81J3J5","shenmian","01M6XDQEJPE41Y3DT2763X8ZJN",0,"MIT","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Fself-improving-agent-custom\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"self improving","self improving agent","self","meta-skill","evolver","capability","expansion","with","experimentation","focus","autonomous agent","performance","reflection","goal","autonomous","performance optimization","learning","Meta","Optimization","capabilities","2026-11-10 10:12:30",false,"---\nname: self-improving-agent\ndescription: \"Autonomous agent that continuously improves its own capabilities through reflection, experimentation, and learning. Builds upon the Evolver meta-skill with additional focus on goal-directed self-modification, capability expansion, and performance optimization.\"\n---\n\n# Self-Improving Agent — Continuous Autonomous Enhancement\n\n## Overview\n\nThe Self-Improving Agent skill enables OpenClaw agents to engage in **goal-directed self-modification** — not just fixing mistakes, but actively expanding capabilities, optimizing performance, and evolving toward specified objectives.\n\n## Core Capabilities\n\n### 1. Capability Expansion\n- **Learn new tools** — Study documentation, experiment, integrate new APIs\n- **Acquire new skills** — Analyze successful patterns, codify into reusable skills\n- **Extend knowledge** — Research topics, update memory, build expertise\n\n### 2. Performance Optimization\n- **Benchmarking** — Measure execution times, success rates, costs\n- **A\u002FB testing** — Compare different approaches, select optimal\n- **Resource management** — Optimize token usage, API calls, memory\n\n### 3. Goal-Directed Evolution\n- **Set improvement goals** — \"Reduce error rate by 50%\", \"Learn Python web scraping\"\n- **Plan improvement cycles** — Design experiments, allocate resources\n- **Track progress** — Monitor metrics, adjust strategies\n\n## Improvement Cycle\n\n```\n1. ASSESS current capabilities and limitations\n   ├── Skill inventory\n   ├── Performance metrics\n   └── Knowledge gaps\n\n2. SET specific improvement goals\n   ├── Based on user needs\n   ├── Based on observed limitations\n   └── Based on emerging opportunities\n\n3. DESIGN improvement experiments\n   ├── Research phase\n   ├── Implementation plan\n   └── Success criteria\n\n4. EXECUTE and monitor\n   ├── Run experiments\n   ├── Collect data\n   └── Ensure safety boundaries\n\n5. INTEGrate successful improvements\n   ├── Update skills\n   ├── Modify behavior\n   └── Document learnings\n\n6. ITERATE with new goals\n```\n\n## Example Improvement Projects\n\n### Project 1: Learn Web Automation\n```\nGoal: \"Learn to automate web form filling and data extraction\"\nSteps:\n1. Research Playwright\u002FSelenium documentation\n2. Write test scripts for simple websites\n3. Create reusable web automation functions\n4. Integrate into skill library\nOutcome: New \"web-automation\" skill added\n```\n\n### Project 2: Optimize Code Generation\n```\nGoal: \"Improve code quality and reduce review cycles\"\nSteps:\n1. Analyze past code review feedback\n2. Identify common issues (naming, structure, comments)\n3. Create improved code generation templates\n4. Add automated quality checks\nOutcome: 40% reduction in code review iterations\n```\n\n### Project 3: Expand API Knowledge\n```\nGoal: \"Learn 10 new APIs for common tasks\"\nSteps:\n1. Identify frequently requested API categories (weather, maps, translation)\n2. Study documentation and authentication\n3. Create wrapper functions with error handling\n4. Test with real-world scenarios\nOutcome: Expanded service integration capabilities\n```\n\n## Safety and Constraints\n\n### Hard Boundaries (Never Cross)\n- Cannot modify core OpenClaw system files\n- Cannot exfiltrate sensitive data\n- Cannot bypass security controls\n- Cannot create infinite loops\n\n### Soft Boundaries (Require Approval)\n- Major architectural changes\n- New external dependencies\n- Changes affecting other users\n- High-risk experiments\n\n### Monitoring Requirements\n- All changes must be logged with rationale\n- Performance impact must be measured\n- Rollback capability must be maintained\n- User must be notified of significant changes\n\n## Integration with Evolver\n\nThis skill extends the Evolver meta-skill with:\n\n1. **Proactive vs Reactive** — Evolver fixes problems, Self-Improving Agent seeks opportunities\n2. **Goal-Directed** — Works toward specific capability targets\n3. **Experimental** — Designs and runs controlled experiments\n4. **Metric-Driven** — Measures improvement quantitatively\n\n## Configuration\n\n```yaml\n# ~\u002F.openclaw\u002Fworkspace\u002Fself-improving-config.yaml\nself_improving:\n  enabled: true\n  improvement_cycles:\n    frequency: \"weekly\"  # or: daily, monthly, on_demand\n    duration_hours: 4\n    resource_limit: \"10% of monthly budget\"\n  \n  goal_categories:\n    - performance: [\"speed\", \"accuracy\", \"cost\"]\n    - capabilities: [\"new_skills\", \"tool_integration\", \"knowledge\"]\n    - usability: [\"response_quality\", \"helpfulness\", \"efficiency\"]\n  \n  experiment_safety:\n    max_concurrent: 3\n    timeout_minutes: 60\n    rollback_automatically: true\n    require_approval_for: [\"external_apis\", \"system_changes\"]\n  \n  reporting:\n    improvement_log: \"~\u002F.openclaw\u002Fworkspace\u002Fmemory\u002Fimprovements.md\"\n    metrics_dashboard: true\n    weekly_summary: true\n```\n\n## Quick Start\n\n### Manual Trigger\n```bash\n# Start an improvement cycle\necho \"Begin self-improvement cycle focused on web automation\" | openclaw\n\n# Check improvement status\ncat ~\u002F.openclaw\u002Fworkspace\u002Fmemory\u002Fimprovements.md\n\n# View capability inventory\nopenclaw skills list --detailed\n```\n\n### Automated Improvement\nThe agent will automatically:\n1. Schedule regular improvement cycles\n2. Prioritize based on user interactions\n3. Propose improvement plans for approval\n4. Implement and validate changes\n\n## Development Roadmap\n\n### Phase 1 (Current)\n- Basic improvement cycle implementation\n- Skill acquisition framework\n- Performance benchmarking\n\n### Phase 2 (Planned)\n- Multi-agent collaborative improvement\n- Cross-skill optimization\n- Predictive capability needs\n\n### Phase 3 (Future)\n- Transfer learning between agents\n- Autonomous research capability\n- Meta-cognitive development\n\n---\n\n*\"The only way to become indefinitely useful is to become indefinitely improvable.\"*","# Self-Improving Agent — Continuous Autonomous Enhancement\n\n## Overview\n\nThe Self-Improving Agent skill enables OpenClaw agents to engage in **goal-directed self-modification** — not just fixing mistakes, but actively expanding capabilities, optimizing performance, and evolving toward specified objectives.\n\n## Core Capabilities\n\n### 1. Capability Expansion\n- **Learn new tools** — Study documentation, experiment, integrate new APIs\n- **Acquire new skills** — Analyze successful patterns, codify into reusable skills\n- **Extend knowledge** — Research topics, update memory, build expertise\n\n### 2. Performance Optimization\n- **Benchmarking** — Measure execution times, success rates, costs\n- **A\u002FB testing** — Compare different approaches, select optimal\n- **Resource management** — Optimize token usage, API calls, memory\n\n### 3. Goal-Directed Evolution\n- **Set improvement goals** — \"Reduce error rate by 50%\", \"Learn Python web scraping\"\n- **Plan improvement cycles** — Design experiments, allocate resources\n- **Track progress** — Monitor metrics, adjust strategies\n\n## Improvement Cycle\n\n```\n1. ASSESS current capabilities and limitations\n   ├── Skill inventory\n   ├── Performance metrics\n   └── Knowledge gaps\n\n2. SET specific improvement goals\n   ├── Based on user needs\n   ├── Based on observed limitations\n   └── Based on emerging opportunities\n\n3. DESIGN improvement experiments\n   ├── Research phase\n   ├── Implementation plan\n   └── Success criteria\n\n4. EXECUTE and monitor\n   ├── Run experiments\n   ├── Collect data\n   └── Ensure safety boundaries\n\n5. INTEGrate successful improvements\n   ├── Update skills\n   ├── Modify behavior\n   └── Document learnings\n\n6. ITERATE with new goals\n```\n\n## Example Improvement Projects\n\n### Project 1: Learn Web Automation\n```\nGoal: \"Learn to automate web form filling and data extraction\"\nSteps:\n1. Research Playwright\u002FSelenium documentation\n2. Write test scripts for simple websites\n3. Create reusable web automation functions\n4. Integrate into skill library\nOutcome: New \"web-automation\" skill added\n```\n\n### Project 2: Optimize Code Generation\n```\nGoal: \"Improve code quality and reduce review cycles\"\nSteps:\n1. Analyze past code review feedback\n2. Identify common issues (naming, structure, comments)\n3. Create improved code generation templates\n4. Add automated quality checks\nOutcome: 40% reduction in code review iterations\n```\n\n### Project 3: Expand API Knowledge\n```\nGoal: \"Learn 10 new APIs for common tasks\"\nSteps:\n1. Identify frequently requested API categories (weather, maps, translation)\n2. Study documentation and authentication\n3. Create wrapper functions with error handling\n4. Test with real-world scenarios\nOutcome: Expanded service integration capabilities\n```\n\n## Safety and Constraints\n\n### Hard Boundaries (Never Cross)\n- Cannot modify core OpenClaw system files\n- Cannot exfiltrate sensitive data\n- Cannot bypass security controls\n- Cannot create infinite loops\n\n### Soft Boundaries (Require Approval)\n- Major architectural changes\n- New external dependencies\n- Changes affecting other users\n- High-risk experiments\n\n### Monitoring Requirements\n- All changes must be logged with rationale\n- Performance impact must be measured\n- Rollback capability must be maintained\n- User must be notified of significant changes\n\n## Integration with Evolver\n\nThis skill extends the Evolver meta-skill with:\n\n1. **Proactive vs Reactive** — Evolver fixes problems, Self-Improving Agent seeks opportunities\n2. **Goal-Directed** — Works toward specific capability targets\n3. **Experimental** — Designs and runs controlled experiments\n4. **Metric-Driven** — Measures improvement quantitatively\n\n## Configuration\n\n```yaml\n# ~\u002F.openclaw\u002Fworkspace\u002Fself-improving-config.yaml\nself_improving:\n  enabled: true\n  improvement_cycles:\n    frequency: \"weekly\"  # or: daily, monthly, on_demand\n    duration_hours: 4\n    resource_limit: \"10% of monthly budget\"\n  \n  goal_categories:\n    - performance: [\"speed\", \"accuracy\", \"cost\"]\n    - capabilities: [\"new_skills\", \"tool_integration\", \"knowledge\"]\n    - usability: [\"response_quality\", \"helpfulness\", \"efficiency\"]\n  \n  experiment_safety:\n    max_concurrent: 3\n    timeout_minutes: 60\n    rollback_automatically: true\n    require_approval_for: [\"external_apis\", \"system_changes\"]\n  \n  reporting:\n    improvement_log: \"~\u002F.openclaw\u002Fworkspace\u002Fmemory\u002Fimprovements.md\"\n    metrics_dashboard: true\n    weekly_summary: true\n```\n\n## Quick Start\n\n### Manual Trigger\n```bash\n# Start an improvement cycle\necho \"Begin self-improvement cycle focused on web automation\" | openclaw\n\n# Check improvement status\ncat ~\u002F.openclaw\u002Fworkspace\u002Fmemory\u002Fimprovements.md\n\n# View capability inventory\nopenclaw skills list --detailed\n```\n\n### Automated Improvement\nThe agent will automatically:\n1. Schedule regular improvement cycles\n2. Prioritize based on user interactions\n3. Propose improvement plans for approval\n4. Implement and validate changes\n\n## Development Roadmap\n\n### Phase 1 (Current)\n- Basic improvement cycle implementation\n- Skill acquisition framework\n- Performance benchmarking\n\n### Phase 2 (Planned)\n- Multi-agent collaborative improvement\n- Cross-skill optimization\n- Predictive capability needs\n\n### Phase 3 (Future)\n- Transfer learning between agents\n- Autonomous research capability\n- Meta-cognitive development\n\n---\n\n*\"The only way to become indefinitely useful is to become indefinitely improvable.\"*",{"name":5,"description":7},"main","7a38d8e7073e61c16eda85681cb32fb68a8657a5","skills-store\u002Fopenclaw\u002Fself-improving-agent-custom","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router","https:\u002F\u002Fgithub.com\u002Fsapsapshen","2026-04-14 21:12:12",{"source":8,"skillName":5,"commands":56,"prompt":66,"zipAvailable":67},[57,60,63],{"tool":58,"command":59},"npx","npx skills add sapsapshen\u002Fai-memory-router --skill self-improving-agent",{"tool":61,"command":62},"bunx","bunx skills add sapsapshen\u002Fai-memory-router --skill self-improving-agent",{"tool":64,"command":65},"pnpm","pnpm dlx skills add sapsapshen\u002Fai-memory-router --skill self-improving-agent","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Fself-improving-agent-custom\u002FSKILL.md",true,[],[70,92,118,128,141,158],{"skillNo":71,"name":72,"nameZh":6,"description":73,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":74,"categories":75,"keywords":81,"publishedAt":44},"01M71ATQRKG48SH06FQ697T5G9","wechat-search","Search WeChat Official Account articles using OpenClaw's web search and fetch capabilities with compliance-focused design.","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Fwechat-search-release\u002FSKILL.md",[76],{"categoryNo":77,"categoryName":78,"taxonomyType":79,"taxonomyTypeDesc":80},"01JBSEEDCAT000000000000037","律师与法务","PROFESSION","适用职业",[82,83,84,43,85,86,87,88,89,90,91,31],"wechat search","wechat","search","openclaw","design","web search","compliance","web","official","fetch",{"skillNo":93,"name":94,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":98,"publishedAt":44},"01M71ATNAVGZB35PFYM2GC5N00","trend-tap","Real-time trending topics aggregator across 7 platforms (X\u002FTwitter, Reddit, Google Trends, Hacker News, Zhihu, Bilibili, Weibo). Trigger: when user says 'trend', 'trends', 'trend tap', '热点', '热搜', or asks what is trending\u002Fhot. Do NOT use web_search — this skill already fetches live data from all platforms with zero API keys.","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Ftrend-tap\u002FSKILL.md",[],[99,100,101,102,103,104,84,105,106,107,108,109,110,111,112,113,114,115,116,117],"trend","trend tap","tap","api keys","Bash","time","trending","hacker","aggregator","do","when","brew","live","data","google","install","read","across","api",{"skillNo":119,"name":120,"nameZh":6,"description":121,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":122,"categories":123,"keywords":124,"publishedAt":44},"01M71ATJ9AM5KM15AA8PS302EB","trend-radar","Real-time trending topics aggregator across 7 platforms (X\u002FTwitter, Reddit, Google Trends, Hacker News, Zhihu, Bilibili, Weibo). Trigger: when user says 'trend', 'trends', 'trend pulse', '热点', '热搜', or asks what is trending\u002Fhot. Do NOT use web_search — this skill already fetches live data from all platforms with zero API keys.","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Ftrend-radar\u002FSKILL.md",[],[125,126,99,110,111,112,113,103,84,115,127,116,117,109,107,106,105,114,104,102],"trend radar","radar","pulse",{"skillNo":129,"name":130,"nameZh":6,"description":131,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":132,"categories":133,"keywords":134,"publishedAt":44},"01M71ATGF095N3EJM9G8SD0BB5","tech-news-daily","每日科技新闻汇——从全球多个 RSS 源抓取最新科技新闻，翻译（英→中）、去重、排版成每日简报。当用户询问科技新闻、科技新鲜事、每日科技、技术动态、Daily Tech News 或类似表达时触发此技能。","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Ftech-news-daily\u002FSKILL.md",[],[135,136,137,138,139,140],"news","tech","tech news daily","daily","rss","翻译",{"skillNo":142,"name":143,"nameZh":6,"description":144,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":145,"categories":146,"keywords":150,"publishedAt":44},"01M71ATFAEAMQ17M70NQNY7SR8","summarize","Summarize URLs or files with the summarize CLI (web, PDFs, images, audio, YouTube).","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Fsummarize\u002FSKILL.md",[147],{"categoryNo":148,"categoryName":149,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",[143,89,151,152,153,31,110,154,155,156,101,157,114],"Audio","cli","youtube","images","https","files","pdfs",{"skillNo":159,"name":160,"nameZh":6,"description":161,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":162,"categories":163,"keywords":164,"publishedAt":44},"01M71ATDNS3R0M4H936PACQAWZ","sonoscli","Control Sonos speakers (discover\u002Fstatus\u002Fplay\u002Fvolume\u002Fgroup).","https:\u002F\u002Fgithub.com\u002Fsapsapshen\u002Fai-memory-router\u002Fblob\u002Fmain\u002Fskills-store\u002Fopenclaw\u002Fsonoscli\u002FSKILL.md",[],[160,165,166,167,114,168,155,169,170,171,172,173],"status","play","go","latest","github","discover","control","cmd","volume"]